What is chain-of-thought prompting, and when would you use it or avoid it?

What the interviewer wants to hear

• Asks the model to reason step by step before answering
• Helps maths, logic and multi-step tasks
• Costs more tokens and time
• Separate reasoning from the final answer for parsing
• Reasoning models may already think internally

Model answer

Chain-of-thought prompting asks the model to work through the problem step by step before giving the answer. Writing out intermediate steps helps on tasks like maths, logic puzzles, multi-condition policy checks or planning, because the model can build on each step instead of jumping to a guess. I would avoid it for simple tasks such as short classification or rewriting, where it only adds tokens, cost and latency. When I use it in an app, I ask for the reasoning in one tag and the final answer in another, so code can parse the answer cleanly. I also check the model: many newer models reason internally, and for those, asking for explicit steps may add little, so I would compare both versions on an eval set before deciding.

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